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Autonomous Think Tank HAD Solutions for the Future
1 © 2018 ANSYS, Inc. June, 24 2019
Realize Your Product Promise®
Gilles GALLEEDirector, Autonomous Vehicle Simulation Solution
ANSYS
Take the lead on the autonomousrace with virtual validation
June 24, Museo Ferrari - Maranello
Self-Driving Cars and Robo-Taxis
Drones and Urban Air Mobility
Autonomous Mobile Robots
The Autonomous Vehicle Revolution is Here
Drastic reduction in fatalities
Global economic impact
Mobility for the immobile
Elimination of traffic jams
600,000 Lives Saved per Year
$7 TN in Global Economic Impact
40 Hours per Person per Year Saved from Traffic Jams
The Race is On To Deliver Disruptive Impact
Five levels of autonomy
Reference: http://safety.trw.com/autonomous-cars-must-progress-through-these-6-levels-of-automation/0104/
Exclusive research byANSYS and SAE
The SAE Automotive Industry Survey Confirmed:
“Public Confidence and Adoption” is the number 1 barrier to the widespread adoption of fully autonomous vehicles
Demonstrating Safety isthe Critical EngineeringChallenge
Demonstrating Safety is the Critical Challenge
Critical challenges
Physical virtual simulation replaces traditional road and track tests that are too costly and take too long to complete
ANSYS is building the only comprehensive solutionfor simulating autonomous vehicle
With the objective to accurately test millions of scenarios
Safety of embedded software
Human factors and HMI in design process
Critical challenges
Physical virtual simulation replaces traditional road and track tests that are too costly and take too long to complete
ANSYS is building the only comprehensive solutionfor simulating autonomous vehicle
With the objective to accurately test millions of scenarios
Safety of embedded software
Human factors and HMI in design process
Autonomous driving technology stack
Image source: http://www.businessinsider.com/how-ubers-driverless-cars-work-2016-9
Central computer makes decisions with machine-learning and control algorithms.
Human Machine Interfaces exchange information with human occupants.
Sensor Fusion boards and software combine sensor signals.
Drive-By-Wire systems turn, accelerate, and brake the car based on control decisions.
PerceptionLIDAR
PerceptionRADAR
PerceptionULTRASONIC
PerceptionCAMERA
CommunicationV2X
CommunicationGPS
Vehicle StateIMU, Etc
Autonomous driving technology stack HARDWARE & SOFTWARESENSING &
COMMUNICATIONFUSION ADAS/AD
FEATURES
Sensor Fusion
FUSION
Localization
HD MapsDATA SET
L1 – L3 Features
ADASFEATURES
Smart Headlights
Drive Monitoring
Etc...
L4 – L5 Features
ADFEATURES
Path Planning
Etc...
VEHICLE
ControlsACTUATORS
WORLD
Display
HMI
Audio Interaction
Haptic Feedback
* ADAS = Advanced Driver Assistance Systems
AD = Automated Driving / Autonomous Driving
Dozens of simulation use cases for developing hardware and software
PerceptionCAMERA
Perception software uses Artificial Intelligence to identify objects
Artificial Intelligence (AI) Key Terms:• Neural Networks (NN)• Convoluted Neural
Networks (CNN)• Deep Learning
PerceptionLIDAR
PerceptionRADAR
Artificial Intelligence is mostly used in perception
Physical Miles Driven Necessary for L2-L5 Validation
Akio Toyoda, President of Toyota @ Paris Auto Show“It is estimated that some 8.8 billion miles of road
testing, are required”
275 Million miles of fault free driving will be required to prove safety equivalent to a human driver
RAND – Driving to Safety Study275M fault free miles needed to
achieve equivalent safety to human driver
Autonomous Vehicles
Physical road testing alone is not a solutionBillions of miles of road testing will be needed to validate the safety of an autonomous vehicle . . .
. . . i.e. 15,000 round trips to the moon
Autonomous Vehicles
-Rand Corporation
Simulation is the Only Practical Solution: Waymo Case Study
Sacha ArnoudDirector of Engineering at Waymo (*):
✓Driverless Cars: 90% Done, 90% Left To Go
✓Industrialization requires 10x the effort
✓Real world driving is critical but what is more important is the ability to simulate.
(*) https://www.forbes.com/sites/chunkamui/2018/02/28/driverless-cars-90-percent-done-90-percent-left-to-go/#671202f820a0
ANSYS AV Simulation Platform
Sensor Models, Radar, Camera, LiDAR, Ultrasound, Speed, GPS, V2X
Simulation Platform architecture development started in 2017
ANSYS and BMW Group Partner to Jointly Create the Industry's First Simulation Tool Chain for Autonomous Driving
Safety of AV Systems ➔ ISO 26262 + SOTIF
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Perception
01
Motion Planning
02
Motion Execution
03
Functional Safety Analysis(FuSa)
Safety of the Intended Functionality (SOTIF)
Hazards due to limitations (sensors, AI...) Hazards induced by SW and HW system faillures
ANSYS solution enables to achieve the million miles of driving scenarios required to achieve the safety of ISO 26262
With a particular attention to the simulation of the sensors (SOTIF)
Video / MIL
Running SOTIF Driving Scenarios in VRXPERIENCE
Sensor simulation in scenario
22
Why are edge cases a problem?
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Perhaps your autonomy can detect 999 out of every 1,000 images with pedestrians that walk on two legs.
But what if it only detected 700 out of every 1,000 images with pedestrians that use wheelchairs?
P ( accident | wheelchair) should be the same as P ( accident | walker)
Mis
s R
ate
False Alarm Rate
So we need to find all the edge cases!
The pedestrian in a wheelchair is an edge case, i.e. a condition that unknowingly poses safety risks.
Edge cases can be caused by…• Weather conditions (snow, rain, wildfire)• Lighting conditions (glare, night, high beams)• Infrastructure (fences, reflective surfaces, statues)• Types of road users (wheelchairs, people in costumes)• Incomplete training of machine learning systems!
Just because you handle one edge case safely doesn’t mean you’ll handle the next one safely, too!
and identify the root causes of these edge cases
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{ “sun glare”, “guardrail” } { “sun glare”, “fence”, “high-visibility vest” }
{ “sun glare”, “guardrail” }
Root causes (“triggering events” per SOTIF) can be hypothesized, validated, mitigated, and verified.
Some Root causes can be surprising
26
“Children”
“Red objects”
“Sensor noise”
“Windshield wipers”“Columns”
“Camouflage”
“Sun glare”
“Bare legs”
These results are from open-source neural networks. Your mileage may vary.
ANSYS SCADE Vision filters through huge data sets to identify real-world edge cases and safety risks
The CNN detects the bicyclist in baseline scene……but detection is weak in augmented scenes,
especially when bicyclist gets close.
Sensor Models
Radar
Camera
LiDAR
ANSYS Sensor Models for Level 2 – Level 5
Physics based simulation is required for virtual validation
Physics based simulation is required for virtual validation
Gaming engines fail to virtually validate because of their lack of physics or measured materials
Camera: Simulation from component design to full scenarios
Component Development
Optical, Thermal, Structural
Design & Analysis
Vehicle Integration
Vision Performance Analysis
Position Optimization
In-driving scenario
Vision System
Test & Validation
Camera
Traditional Rendering Engine ANSYS’s Physically Accurate Simulation
Camera: Real world fault detection - solar glare
Sensors Fails No Sun Glare
Detected
Requires further physical testing on
road
Camera
ANSYS’s physically accurate, real time driving scenario simulations mixing multi-physics sensors for SIL and HIL testing applications.
AUTONOMOUS VEHICLES
Camera & Radar simulations in driving scenarios
Radar
Radar + Camera simulation in driving scenario
Critical challenges
Physical virtual simulation replaces traditional road and track tests that are too costly and take too long to complete
ANSYS is building the only comprehensive solutionfor simulating autonomous vehicle
With the objective to accurately test millions of scenarios
Safety of embedded software
Human factors and HMI in design process
Interfaces In Automotive
Connected car leadsto more distraction
Levels 2 to 4 of autonomy make the transition to autonomy risky
Complexity of HMI
36
Car’s HMI has to be intuitive and easy to use especially in critical situations
Interfaces in Automotive
Human – Vehicle – Environment: « Closed Loop » in VR
Objectives:➢Zero physical prototypes➢Yet complete validation
Expected benefits:• Driver in the loop (safety, distraction,
mobility efficiency, user experience)• Capitalize user feedback and data• Better match customer’s expectations• Improve & Refine HMI design and
procedures• Explore new concepts faster
HUMAN
VEHICLEENVIRONMENT
Simulation of Light and Human Vision
AVTOL
Automatic Vertical Take-Off and Landing
Airbus and ANSYS Partner to Enable Autonomous Flight to Support Future Combat Air System by 2030
AutonomousTractor
Initial work
Examples – off-road and agriculture use cases 1/2
Forward and rear vision camera – field scenario Headlamp – night drive scenario
Examples – off-road and agriculture use cases 2/2
Front camera and LiDAR – digger scenario Front camera and LiDAR – tractor scenario
Safely Transition To AV Thanks To Simulation
Physical virtual simulation replaces traditional road and track tests that are too costly and take too long to complete
ANSYS is building the only comprehensive solutionfor simulating autonomous vehicle
With the objective to accurately test millions of scenarios
Physical virtual simulation replaces traditional road and track tests that are too costly and take too long to complete
ANSYS is building the only comprehensive solutionfor simulating autonomous vehicles
Virtual simulation enables to take human factors into account all along the design process
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